{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreigzchapadp2nbhpp7xj56c2ro3lefknf3gkfgqe227jugot7bnmsu",
    "uri": "at://did:plc:wwyqal4cnqhuwyacdj7rqq3n/app.bsky.feed.post/3miegoswxeqw2"
  },
  "path": "/t/is-the-use-of-conditional-logistic-regression-necessary-in-case-control-study/28674#post_7",
  "publishedAt": "2026-03-31T06:53:31.000Z",
  "site": "https://discourse.datamethods.org",
  "tags": [
    "@f2harrell"
  ],
  "textContent": "You are right, @f2harrell . I should have been more specific about the source of the random effects. I was thinking of multi-center studies or scenarios where patients are nested within specific surgeons or clinics.\n\nMy main point was that, instead of traditional matching and simple tests, it seems more robust to use a model that accounts for both individual confounders (as fixed effects) and potential clustering—like hospitals or surgeons—(as random effects) if the study setup warrants it. Would you agree that this approach preserves more information and provides a more accurate estimate of the exposure effect compared to traditional pair-matching analysis?",
  "title": "Is the use of conditional logistic regression necessary in case-control study?"
}